Traffic Steering (TS) dynamically allocates user traffic across cells to enhance Quality of Experience (QoE), load balance, and spectrum efficiency in 5G networks. However, TS algorithms remain vulnerable to adversarial conditions such as interference spikes, handover storms, and localized outages. To address this, an AI-driven fuzz testing framework based on the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is proposed to systematically expose hidden vulnerabilities. Using NVIDIA Sionna, five TS algorithms are evaluated across six scenarios. Results show that AI-driven fuzzing detects 34.3% more total vulnerabilities and 5.8% more critical failures than traditional testing, achieving superior diversity and edge-case discovery. The observed variance in critical failure detection underscores the stochastic nature of rare vulnerabilities. These findings demonstrate that AI-driven fuzzing offers an effective and scalable validation approach for improving TS algorithm robustness and ensuring resilient 6G-ready networks.

翻译：流量导向（TS）通过动态分配用户流量至不同小区，以提升5G网络中的体验质量（QoE）、负载均衡与频谱效率。然而，TS算法在面对干扰尖峰、切换风暴及局部中断等对抗性条件时仍存在脆弱性。为此，本文提出一种基于非支配排序遗传算法II（NSGA-II）的AI驱动模糊测试框架，以系统性揭示潜在漏洞。利用NVIDIA Sionna平台，我们在六种场景下对五种TS算法进行了评估。结果表明，相较于传统测试方法，AI驱动的模糊测试能多检测出34.3%的总漏洞及5.8%的关键故障，并展现出更优的多样性与边缘案例发现能力。关键故障检测中观察到的方差揭示了罕见漏洞的随机性本质。这些发现证明，AI驱动的模糊测试为提升TS算法鲁棒性、构建具备韧性的6G就绪网络提供了一种高效且可扩展的验证方法。